%0 Journal Article
%@ 2291-9694
%I JMIR Publications
%V 8
%N 5
%P e16528
%T An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study
%A Lee,Yi-Lien
%A Chou,Willy
%A Chien,Tsair-Wei
%A Chou,Po-Hsin
%A Yeh,Yu-Tsen
%A Lee,Huan-Fang
%+ Department of Nursing, College of Medicine, National Cheng Kung University, 988 Chung Hwa Road, Yung Kung District, Tainan, Taiwan, 886 62812811, Eamonn0330@gmail.com
%K nurse burnout
%K MBI-HSS Chinese version
%K receiver operating characteristic curve
%K convolutional neural network
%K Lz person fit statistic
%D 2020
%7 7.5.2020
%9 Original Paper
%J JMIR Med Inform
%G English
%X Background: Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute’s well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature. Objective: The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage. Methods: We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO−, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model’s 38 estimated parameters for a website assessment. Results: We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model’s predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study. Conclusions: The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.
%M 32379050
%R 10.2196/16528
%U https://medinform.jmir.org/2020/5/e16528
%U https://doi.org/10.2196/16528
%U http://www.ncbi.nlm.nih.gov/pubmed/32379050